VisionQuest Biomedical (United States)

Abstract Lesbian, gay, bisexual, transgender, and queer (LGBTQ) persons' experiences of violence continue to be inadequately addressed. We present secondary data analysis of violence experienced by...

Cold provocation experiments were conducted to evaluate empirically a thermoregulation model of the plantar foot. An infrared camera recorded the temperature of the feet after removal of a cold patch to document the recovery process in order to validate the non-linear thermoregulation model. To do so, four healthy controls and four diabetic peripheral neuropathy (DPN) subjects were recruited. The thermal response of six areas of the plantar foot was analyzed. The non-linear thermoregulation model is compared to Newton's simple cooling model. The experimental data showed a better performance of the thermoregulation model in the classification of subjects with DPN. In addition, the proposed approach shows a better correlation with areas in the foot with signs of neuropathy.

The purpose of this paper is to present a novel approach for extracting image-based features for classifying age-related macular degeneration (AMD) in digital retinal images. 100 retinal images were classified by an ophthalmologist into 12 categories based on the visual characteristics of the disease. Independent Component Analysis (ICA) was used to extract features at different spatial scales to be used as input to a classifier. The classification used a type of regression, partial least squares. In this experiment ICA replicated the ophthalmologist's visual classification by correctly assigning all 12 images from two of the classes.

This paper presents a new Independent Component Analysis algorithm (ICA-P) for modeling and measuring physiological responses to optical stimulation from a new, non-invasive optical imaging device. The ICA-P algorithm uses prior information on the visual stimulus to provide improved detection performance. In comparison with other methods that were considered (JADE, Infomax, SOBI, ESD, and Fast-ICA), ICA-P is shown to achieve significantly improved performance in detecting the retinal response to visual stimuli. Results from simulations show that we can estimate retinal reflectance changes as small as 0.01% (-40 dB, 10 dB lower than the other considered methods).

In the early stages of some retinal diseases, such as glaucoma, loss of retinal activity may be difficult to detect with today's clinical instruments. Many of today's instruments focus on detecting changes in anatomical structures, such as the nerve fiber layer. Our device, which is based on a modified fundus camera, seeks to detect changes in optical signals that reflect functional changes in the retina. The functional imager uses a patterned stimulus at wavelength of 535nm. An intrinsic functional signal is collected at a near infrared wavelength. Measured changes in reflectance in response to the visual stimulus are on the order of 0.1% to 1% of the total reflected intensity level, which makes the functional signal difficult to detect by standard methods because it is masked by other physiological signals and by imaging system noise. In this paper, we analyze the video sequences from a set of 60 experiments with different patterned stimuli from cats. Using a set of statistical techniques known as Independent Component Analysis (ICA), we estimate the signals present in the videos. Through controlled simulation experiments, we quantify the limits of signal strength in order to detect the physiological signal of interest. The results of the analysis show that, in principle, signal levels of 0.1% (-30dB) can be detected. The study found that in 86% of the animal experiments the patterned stimuli effects on the retina can be detected and extracted. The analysis of the different responses extracted from the videos can give an insight of the functional processes present during the stimulation of the retina.